Managing Global Resources for a Secure Future

2017 Annual Meeting | Oct. 22-25 | Tampa, FL

106982 Predicting Soil Organic C and N in the Russian Chernozem from Wireless Color Sensor Measurements.

Poster Number 1516

See more from this Division: ASA Section: Land Management and Conservation
See more from this Session: Soil Health for Agroecosystems Poster (includes student competition)

Tuesday, October 24, 2017
Tampa Convention Center, East Exhibit Hall

Elena Mikhailova1, roxanne stiglitz2, Christopher Post2, Mark A. Schlautman3, Patrick Gerard4 and Julia Sharp5, (1)261 Lehotsky Hall, Clemson University, Clemson, SC
(2)Forestry and Environmental Conservation, Clemson University, Clemson, SC
(3)Clemson University, Anderson, SC
(4)Department of Mathematical Sciences, Clemson University, Clemson, SC
(5)Department of Statistics, Colorado State University, Fort Collins, CO, United States Minor Outlying Islands
Abstract:
Color sensor technologies offer opportunities for affordable and rapid assessment of soil organic carbon (SOC) and total nitrogen (TN) in the field, but applicability of these technologies may vary by soil type and land use. The objective of this study was to use an inexpensive color sensor to develop SOC and TN prediction models for the Russian Chernozem (Haplic Chernozem) under two management regimes: a native grassland (not cultivated for at least 300 years), and an adjacent continuous fallow field in the Kursk region of Russia. Twenty-one dried soil samples were analyzed using a Nix ProTM color sensor that is controlled through a mobile application and Bluetooth to collect CIEL*a*b* (darkness to lightness, green to red, and blue to yellow) color data. The data were used to develop the natural log of SOC (lnSOC) and TN (lnTN) prediction models using depth, L*, a*, and b* for each sample were used as predictor variables in regression analyses. Resulting residual plots, root mean square errors (RMSE), and coefficients of determination (R2) were used to asses model fit for the SOC and total N prediction models. The final models for all soil samples, which included depth and color parameters, for lnSOC (R2=0.968, RMSE=0.006, p-value<0.001) and lnTN (R2=0.959, RMSE=0.009, p-value<0.001) for all samples contained depth, L*, and a* as predictor variables. The final models for native soil samples, which included depth and color parameters, for lnSOC (R2=0.980, RMSE=0.004, p-value<0.001) and lnTN (R2=0.971, RMSE=0.007, p-value<0.001) for all samples contained depth and a* as predictor variables. The final models for all soil samples, which included only color parameters, for lnSOC (R2=0.817, RMSE=0.033, p-value<0.001) and lnTN (R2=0.792, RMSE=0.042, p-value<0.001) for all samples contained L*, and a* as predictor variables. The final models for native soil samples, which included only color parameters, for lnSOC (R2=0.891, RMSE=0.022, p-value<0.001) and lnTN (R2=0.877, RMSE=0.029, p-value<0.001) for all samples contained L* and a* as predictor variables. The results suggest that soil color may be used for rapid assessment of TN and SOC regardless of soil management practices.

See more from this Division: ASA Section: Land Management and Conservation
See more from this Session: Soil Health for Agroecosystems Poster (includes student competition)